This paper presents a new model based on the Monte Carlo simulation method for considering the uncertainty of electric vehicles’ charging station’s load in a day-ahead operation optimization of a smart micro-grid. In the proposed model, some uncertain effective factors on the electric vehicles’ charging station’s load including battery capacity, type of electric vehicles, state of charge, charging power level and response to energy price changes are considered. In addition, other uncertainties of operating parameters such as market price, photovoltaic generation and loads are also considered. Therefore, various stochastic scenarios are generated and involved in a cost minimization problem, which is formulated in the form of mixed-integer linear programming. Finally, the proposed model is simulated on a typical micro-grid with two 60 kW micro-turbines, a 60 kW photovoltaic unit and some loads. The results showed that by applying the proposed model for estimation of charging station load, the total operation cost decreased.

The proton exchange membrane fuel cell as a green power source is a suitable replacement of the engine mounted generators in the emergency power unit of a more-electric aircraft. Most existing energy management methods for operation of fuel cells in the more-electric aircraft refer to the hydrogen consumption minimization. But due to the increasing number of electrical components and hence electrical demand in the aircraft, demand-side management should be considered in these methods. In order to determine the effect of demand-side management on the fuel cell operation and size, an efficient load priority model is presented and integrated into an optimization framework. The proposed optimization framework is formulated as mixed-integer quadratic programming using Karush–Kuhn–Tucker optimality condition and is solved by CPLEX optimization tool. The Boeing 787 electrical distribution system is considered as a single-bus case study to evaluate the performance of the proposed optimization framework. Numerical results show that the size of fuel cell as an emergency power unit resource depends on the type and importance of the system’s loads in different emergency conditions. Also, with an efficient priority model, both hydrogen consumption and load shedding can be decreased.